Gastric Cancer (GC)NCT07190040
Integrating Multi-Omics Data for Enhanced Prognosis Prediction in Gastric Cancer Post-Neoadjuvant Therapy
An observational study of Gastric Cancer (GC), run by Chang-Ming Huang, Prof. at 1 site in China.
Trial summary
Study Protocol: Integrating Multi-Omics Data for Prognosis Prediction in Gastric Cancer Post-Neoadjuvant Therapy Objective: To develop and validate an integrative prognostic nomogram for patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant therapy, combining deep learning-derived radiomic… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Gastric Cancer (GC)
- How many people
- 179 participants
- Where
- 1 siteChina
- Study dates
- 1 Jan 2019to 1 Sep 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- Adult, Older adult
Study population: Patients with locally advanced gastric cancer who underwent neoadjuvant therapy
Who may be able to join
- Gastric adenocarcinoma confirmed pathologically via gastroscopy;
- Clinical staging of cT3/T4N0/+M0 with a history of receiving at least two cycles of neoadjuvant therapy
- No prior history of other malignant tumors
- Completion of radical gastrectomy
Who cannot join
- Gastric cancer originating from the remnant stomach
- Absence of baseline computed tomography (CT) data prior to treatment or suboptimal CT image quality that could compromise the accuracy of radiomic information extraction
- Absence of postoperative transcriptome data
Trial locations and contacts
1 site in 1 country.
About this study
Study Protocol: Integrating Multi-Omics Data for Prognosis Prediction in Gastric Cancer Post-Neoadjuvant Therapy
Objective:
Read more
To develop and validate an integrative prognostic nomogram for patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant therapy, combining deep learning-derived radiomic features (DeepScore), transcriptome-based immune scores (ImmuneScore), and ypTNM staging.
Study Design:
A retrospective, single-center cohort study.
Participants:
A total of 179 LAGC patients who received neoadjuvant therapy followed by radical gastrectomy at Fujian Medical University Union Hospital between January 2019 and December 2022. Patients were divided into a training cohort (n = 125) and an independent validation cohort (n = 54).
Data Collection:
Baseline contrast-enhanced CT scans prior to neoadjuvant therapy were used for radiomic analysis. Postoperative tumor RNA sequencing data were used for immune profiling. Clinical and pathological data, including ypTNM stage, were collected from medical records.
Methods:
DeepScore: Extracted from CT images using a ResNet18-based deep learning model. Significant features were selected via univariate Cox and LASSO regression.
ImmuneScore: Calculated from RNA-seq data using the ESTIMATE algorithm to assess tumor immune infiltration.
Nomogram Construction: A multi-omics nomogram was developed using multivariate Cox regression incorporating DeepScore, ImmuneScore, and ypTNM stage.
Validation: Model performance was evaluated using time-dependent ROC analysis (AUC) and Kaplan-Meier survival analysis with log-rank tests in both cohorts.
Primary Outcomes:
Disease-free survival (DFS) and overall survival (OS).
Statistical Analysis:
Survival analyses were performed using Kaplan-Meier and Cox regression models. AUC values were computed for 1-, 2-, and 3-year DFS predictions. All analyses were conducted in R (v4.4.3).
- Conditions
- Gastric Cancer (GC)
- Medical subject headings
- Stomach Neoplasms
What is being measured
The primary outcome is the main question the study is designed to answer.
the Area Under the Curve
The model's predictive accuracy was evaluated by computing the Area Under the Curve for predicting 1-year, 2-year, and 3-year disease-free survival.
Measured: 2023.01.31-2025.05.31
Secondary outcomes (1)
Disease-free survival
The log-rank test was utilized to compare disease-free survival and overall survival curves between these groups.
Measured: 2023.01.31-2025.05.31
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Sep 2025
- Study start1 Jan 2019
- Primary completion31 Dec 2022
- Study completion1 Sep 2025
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gastric Cancer (GC)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Retrospective
- Enrolment
- 179 participants (actual)
Sponsor and investigators
- Lead sponsor
- Chang-Ming Huang, Prof.
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor-investigator, Chang-Ming Huang, Prof., Fujian Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07190040
- Sponsor's protocol ID
- 2025NAT01
- Status last verified
- Sep 2025
- First submitted
- 13 Sep 2025
- Last updated
- 24 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Integrating Multi-Omics Data for Enhanced Prognosis Prediction in Gastric Cancer Post-Neoadjuvant Therapy
- ClinicalTrials.gov ID
- NCT07190040
- Acronym / sponsor ID
- 2025NAT01
- Conditions
- Gastric Cancer (GC)
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 179 participants (actual)
- Lead sponsor
- Chang-Ming Huang, Prof.University, hospital or other
- Study start
- 1 Jan 2019 (actual)
- Primary completion
- 31 Dec 2022 (actual)
- Study completion
- 1 Sep 2025 (actual)
- First posted
- 24 Sep 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
Study Protocol: Integrating Multi-Omics Data for Prognosis Prediction in Gastric Cancer Post-Neoadjuvant Therapy
Objective:
To develop and validate an integrative prognostic nomogram for patients with locally advanced gastric cancer (LAGC) undergoing neoadjuvant therapy, combining deep learning-derived radiomic features (DeepScore), transcriptome-based immune scores (ImmuneScore), and ypTNM staging.
Study Design:
A retrospective, single-center cohort study.
Participants:
A total of 179 LAGC patients who received neoadjuvant therapy followed by radical gastrectomy at Fujian Medical University Union Hospital between January 2019 and December 2022. Patients were divided into a training cohort (n = 125) and an independent validation cohort (n = 54).
Data Collection:
Baseline contrast-enhanced CT scans prior to neoadjuvant therapy were used for radiomic analysis. Postoperative tumor RNA sequencing data were used for immune profiling. Clinical and pathological data, including ypTNM stage, were collected from medical records.
Methods:
DeepScore: Extracted from CT images using a ResNet18-based deep learning model. Significant features were selected via univariate Cox and LASSO regression.
ImmuneScore: Calculated from RNA-seq data using the ESTIMATE algorithm to assess tumor immune infiltration.
Nomogram Construction: A multi-omics nomogram was developed using multivariate Cox regression incorporating DeepScore, ImmuneScore, and ypTNM stage.
Validation: Model performance was evaluated using time-dependent ROC analysis (AUC) and Kaplan-Meier survival analysis with log-rank tests in both cohorts.
Primary Outcomes:
Disease-free survival (DFS) and overall survival (OS).
Statistical Analysis:
Survival analyses were performed using Kaplan-Meier and Cox regression models. AUC values were computed for 1-, 2-, and 3-year DFS predictions. All analyses were conducted in R (v4.4.3).
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Age groups
- Adult, Older adult
- Study population
- Patients with locally advanced gastric cancer who underwent neoadjuvant therapy
Key inclusion criteria (4 listed)
- Gastric adenocarcinoma confirmed pathologically via gastroscopy;
- Clinical staging of cT3/T4N0/+M0 with a history of receiving at least two cycles of neoadjuvant therapy
- No prior history of other malignant tumors
- Completion of radical gastrectomy
Key exclusion criteria (3 listed)
- Gastric cancer originating from the remnant stomach
- Absence of baseline computed tomography (CT) data prior to treatment or suboptimal CT image quality that could compromise the accuracy of radiomic information extraction
- Absence of postoperative transcriptome data
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
the Area Under the Curve
Time frame: 2023.01.31-2025.05.31
The model's predictive accuracy was evaluated by computing the Area Under the Curve for predicting 1-year, 2-year, and 3-year disease-free survival.
1 secondary outcome
Disease-free survival
Time frame: 2023.01.31-2025.05.31
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Sep 2025.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gastric Cancer (GC), run by Chang-Ming Huang, Prof. at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 24 Sep 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
the Area Under the Curve (time frame: 2023.01.31-2025.05.31). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Sep 2025.